READ-ONLY. src/ and web/ untouched; 4,159 tests still green.
WHAT THE CHAMPION IS. probabilityEstimator is five lines of arithmetic: the
empirical frequency of (stat > THIS line) over the game log, blended 0.6/0.4
with the last-5 frequency, then +/-0.03 opponent, +/-0.015 home/away, a
cv>0.40 pull toward 0.50, and a clamp to [0.10, 0.95]. It reads three
features. featureCache retains a dozen more that p_win never touches.
THE ABLATION IS EXACT, NOT A REFIT. Every adjustment is closed-form from
stored features and the consistency step is linear, so each layer subtracts
algebraically out of the stored p_win -- no re-estimation, no re-fetch, no
lookahead possible. Per stat, paired bootstrap:
removing ALL THREE adjustments changes resolution by NOTHING on every stat
hits -0.0059 total_bases -0.0015 rbi +0.0106 runs +0.0130 walks +0.0008
and rbi's home/away is mildly HARMFUL (+0.0053, CI excludes zero). So ~100% of
the champion's resolution is base+recency: how often this player has cleared
this number lately. Everything else is decoration.
A CORRECTION. Pooled, the champion resolves 0.46; per stat it is 0.196 (hits)
to 0.499 (rbi). Pooling stats with different base rates inflates correlation,
so 0.46 should not be quoted as the champion's resolution. Last session's
paired differences remain valid; only the absolute level was inflated.
THE BIGGEST LOSS IS NOT A MISSING FEATURE -- IT IS THE CLAMP. 358 of 1,741
settled rows (20.6%) sit on the boundary, so the model emits a constant there
and cannot rank a fifth of the book at all. And that constant hides two
opposite failures: 0.900 covers home_runs-under truly winning 99.5% (9.5pts
under-confident) next to hits-under truly winning 51.9% (38.1pts over-
confident). PROB_CEIL=0.95 makes the 99.5% case inexpressible. Global
over-prediction is +3.5pts, +7.6 on total_bases. None of this needs new data.
ONE REAL MISSING-WEIGHTING LEAD: opportunity_drift, residual corr +0.156 on
hits and +0.145 on total_bases -- it REPEATS across independent stats, unlike
the weather hits on TB which sit inside the expected false-positive count (70
tests at alpha .05 expects 3-4). And we already compute it: arch-v1's
opportunity axis uses it and extracts nothing (delta +0.0001). Wrong
implementation, not a missing feature -- opportunity must scale the rate, not
nudge the probability.
ARCHETYPE IS UNMEASURABLE, NOT REFUTED. Only 2 of 41 labels (BOMBER, GHOST)
reach n>=40 settled rows and every mean residual straddles zero. That is "we
have not measured it", and it does not license acting in either direction.
Why every challenger has failed is now legible: the ladder and hits-v1 REPLACE
the frequency question with a fitted distribution; the environment axis adds
inputs the champion ignores. Asking the frequency question at the traded line
is the thing that works.
Flagged, not fixed: model_snapshots.outcome is NULL on all 22,032 rows -- the
retention table built for exactly this replay was never settled, so labels had
to be joined from ledger_entries.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01W1sivYNqY2TS5ftykmHBU9
builtbykev
d8bf7765db